The role of context in our daily interaction with our environment has been studied in psychology, linguistics, artificial intelligence, information retrieval, and more recently, in pervasive/ubiquitous computing. However, context has been largely ignored in research into recommender systems specifically and personalization in general. In this paper we describe how context can be brought to bear on recommender systems. As a means for achieving this, we propose a fundamental shift in terms of how we model a user within a recommendation system: inspired by models of human memory developed in psychology, we distinguish between a user’s short term and long term memories, define a recommendation process that uses these two memories, using context-based retrieval cues to retrieve relevant preference information from long term memory and use it in conjunction with the information stored in short term memory for generating recommendations. We also describe implementations of recommender systems and personalization solutions based on this framework and show how this results in an increase in recommendation quality.